Relax precision in pysawsim.histogram.Histogram bin_edges doctest.

[sawsim.git] / pysawsim / histogram.py

diff --git a/pysawsim/histogram.py b/pysawsim/histogram.py
index 1b743eebed8c7053e37db477369513db27b3ac5f..acd135955e1223df0a0b0f591ceb8849cee54230 100644 (file)
--- a/pysawsim/histogram.py
+++ b/pysawsim/histogram.py
@@ -35,6 +35,9 @@ class Histogram (object):
 
     >>> h = Histogram()
     """
+    def __init__(self):
+        self.headings = None
+
     def calculate_bin_edges(self, data, bin_width):
         """
         >>> h = Histogram()
@@ -59,17 +62,16 @@ class Histogram (object):
 
         All bins should be of equal width (so we can calculate which
         bin a data point belongs to).
-
-        `data` should be a numpy array.
         """
-        self.headings = None
-        self.bin_edges = bin_edges
+        data = numpy.array(data)
+        self.bin_edges = numpy.array(bin_edges)
         bin_width = self.bin_edges[1] - self.bin_edges[0]
 
         bin_is = numpy.floor((data - self.bin_edges[0])/bin_width)
-        self.counts = []
-        for i in range(len(self.bin_edges)-1):
-            self.counts.append(sum(bin_is == i).sum())
+        self.counts = numpy.zeros((len(self.bin_edges)-1,), dtype=numpy.int)
+        for i in range(len(self.counts)):
+            self.counts[i] = (bin_is == i).sum()
+        self.counts = numpy.array(self.counts)
         self.total = float(len(data)) # some data might be outside the bins
         self.mean = data.mean()
         self.std_dev = data.std()
@@ -102,7 +104,7 @@ class Histogram (object):
         >>> h.counts
         [10.0, 40.0, 5.0]
         >>> h.bin_edges  # doctest: +ELLIPSIS
-        [1.5e-10, 2.000...e-10, 2.500...e-10, 3e-10]
+        [1.5e-10, 2...e-10, 2.5...e-10, 3e-10]
         >>> h.probabilities  # doctest: +ELLIPSIS
         [0.181..., 0.727..., 0.0909...]
         """
@@ -180,14 +182,14 @@ class Histogram (object):
         return abs(other.std_dev - self.std_dev)
 
     def chi_squared_residual(self, other):
-        assert self.bin_edges == other.bin_edges
+        assert (self.bin_edges == other.bin_edges).all()
         residual = 0
         for probA,probB in zip(self.probabilities, other.probabilities):
             residual += (probA-probB)**2 / probB
         return residual
 
     def jensen_shannon_residual(self, other):
-        assert self.bin_edges == other.bin_edges
+        assert (self.bin_edges == other.bin_edges).all()
         def d_KL_term(p,q):
             """
             Kullback-Leibler divergence for a single bin, with the